Data & AI Modeler

Company Overview

Our client is a global, regulated financial services group with a strong international presence across trading, fintech, digital assets, and technology-led financial products. Headquartered in Dubai, the organisation operates across multiple jurisdictions and serves a large global client base through advanced, secure, and scalable digital platforms.

Role Overview

We are seeking a Data & AI Modeler to own the design, implementation, and governance of enterprise data models that support AI systems, business intelligence, analytics, reporting, and machine learning initiatives. This is a specialist role at the intersection of data architecture and AI enablement. The successful candidate will design scalable dimensional models, Data Vault schemas, semantic layers, and feature data foundations that ensure data is clean, governed, reusable, and semantically consistent across the organisation.

You will work closely with Data Engineers, BI Analysts, Data Scientists, AI Engineers, Product teams, and business stakeholders to create a trusted data foundation that accelerates analytical and AI use cases across key business domains, including customers, trading activity, transactions, products, financial data, and digital engagement.

Key Responsibilities

Data Modelling & Architecture

• Design and maintain enterprise data models across core business domains, including customers, transactions, products, trading activity, events, financial data, and operational processes.
• Develop dimensional models using Kimball methodologies, including star schemas, snowflake schemas, and wide denormalised models tailored to different analytical and reporting needs.
• Design and implement Data Vault 2.0 models, including hubs, links, satellites, and business vault structures.
• Partner with Data Engineers to ensure data models are implemented efficiently and scalably across cloud data platforms such as Databricks, Snowflake, BigQuery, or Redshift.
• Ensure data models are extensible, well-documented, performant, and suitable for both BI and AI consumption.

Semantic Layer & Metric Governance

• Design and maintain governed semantic and metrics layers using dbt Metrics, LookML, or equivalent technologies.
• Establish canonical definitions for business metrics, KPIs, dimensions, hierarchies, calculations, and business logic.
• Ensure key business metrics are documented, auditable, consistently applied, and reused across BI dashboards, reporting, analytics, and AI systems.
• Work with BI and Analytics teams to expose governed datasets through reporting tools with appropriate access controls, definitions, and documentation.
• Reduce inconsistencies in reporting by creating a trusted, centralised data layer for enterprise consumption.

AI & Machine Learning Data Architecture

• Design data foundations for machine learning and AI use cases, including reliable feature datasets for model training, validation, inference, and monitoring.
• Build point-in-time correct datasets that prevent data leakage and support reproducible model developme
• nt.Support feature store design and integration using technologies such as Feast, AWS SageMaker Feature Store, or custom feature platform solutions.
• Enable both offline historical feature generation and online, low-latency feature serving where required.
• Work closely with AI Engineers and Data Scientists to ensure training datasets, labels, features, and transformations are version controlled and production ready.

Data Quality, Governance & Lineage

• Implement automated data quality frameworks using dbt tests, Great Expectations, and related technologies.
• Define and monitor quality controls covering schema validation, null rates, referential integrity, freshness, duplication, reconciliation, and business-rule validation.
• Build and maintain end-to-end lineage documentation from source syste